From b5f341332f05d88c0ec64926f39b18a356e60461 Mon Sep 17 00:00:00 2001
From: Brock <jbrockmendel@gmail.com>
Date: Mon, 9 Nov 2020 12:58:53 -0800
Subject: [PATCH] REF: collect boilerplate in _datetimelike_compat

---
 pandas/core/nanops.py | 53 ++++++++++++++++++++++++-------------------
 1 file changed, 30 insertions(+), 23 deletions(-)

diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index cfb02e5b1e987..d9e51810f1445 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -367,6 +367,32 @@ def _wrap_results(result, dtype: np.dtype, fill_value=None):
     return result
 
 
+def _datetimelike_compat(func):
+    """
+    If we have datetime64 or timedelta64 values, ensure we have a correct
+    mask before calling the wrapped function, then cast back afterwards.
+    """
+
+    @functools.wraps(func)
+    def new_func(values, *, axis=None, skipna=True, mask=None, **kwargs):
+        orig_values = values
+
+        datetimelike = values.dtype.kind in ["m", "M"]
+        if datetimelike and mask is None:
+            mask = isna(values)
+
+        result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)
+
+        if datetimelike:
+            result = _wrap_results(result, orig_values.dtype, fill_value=iNaT)
+            if not skipna:
+                result = _mask_datetimelike_result(result, axis, mask, orig_values)
+
+        return result
+
+    return new_func
+
+
 def _na_for_min_count(
     values: np.ndarray, axis: Optional[int]
 ) -> Union[Scalar, np.ndarray]:
@@ -480,6 +506,7 @@ def nanall(
 
 
 @disallow("M8")
+@_datetimelike_compat
 def nansum(
     values: np.ndarray,
     *,
@@ -511,25 +538,18 @@ def nansum(
     >>> nanops.nansum(s)
     3.0
     """
-    orig_values = values
-
     values, mask, dtype, dtype_max, _ = _get_values(
         values, skipna, fill_value=0, mask=mask
     )
     dtype_sum = dtype_max
-    datetimelike = False
     if is_float_dtype(dtype):
         dtype_sum = dtype
     elif is_timedelta64_dtype(dtype):
-        datetimelike = True
         dtype_sum = np.float64
 
     the_sum = values.sum(axis, dtype=dtype_sum)
     the_sum = _maybe_null_out(the_sum, axis, mask, values.shape, min_count=min_count)
 
-    the_sum = _wrap_results(the_sum, dtype)
-    if datetimelike and not skipna:
-        the_sum = _mask_datetimelike_result(the_sum, axis, mask, orig_values)
     return the_sum
 
 
@@ -552,6 +572,7 @@ def _mask_datetimelike_result(
 
 @disallow(PeriodDtype)
 @bottleneck_switch()
+@_datetimelike_compat
 def nanmean(
     values: np.ndarray,
     *,
@@ -583,8 +604,6 @@ def nanmean(
     >>> nanops.nanmean(s)
     1.5
     """
-    orig_values = values
-
     values, mask, dtype, dtype_max, _ = _get_values(
         values, skipna, fill_value=0, mask=mask
     )
@@ -592,9 +611,7 @@ def nanmean(
     dtype_count = np.float64
 
     # not using needs_i8_conversion because that includes period
-    datetimelike = False
     if dtype.kind in ["m", "M"]:
-        datetimelike = True
         dtype_sum = np.float64
     elif is_integer_dtype(dtype):
         dtype_sum = np.float64
@@ -616,9 +633,6 @@ def nanmean(
     else:
         the_mean = the_sum / count if count > 0 else np.nan
 
-    the_mean = _wrap_results(the_mean, dtype)
-    if datetimelike and not skipna:
-        the_mean = _mask_datetimelike_result(the_mean, axis, mask, orig_values)
     return the_mean
 
 
@@ -875,7 +889,7 @@ def nanvar(values, *, axis=None, skipna=True, ddof=1, mask=None):
     # precision as the original values array.
     if is_float_dtype(dtype):
         result = result.astype(dtype)
-    return _wrap_results(result, values.dtype)
+    return result
 
 
 @disallow("M8", "m8")
@@ -930,6 +944,7 @@ def nansem(
 
 def _nanminmax(meth, fill_value_typ):
     @bottleneck_switch(name="nan" + meth)
+    @_datetimelike_compat
     def reduction(
         values: np.ndarray,
         *,
@@ -938,13 +953,10 @@ def reduction(
         mask: Optional[np.ndarray] = None,
     ) -> Dtype:
 
-        orig_values = values
         values, mask, dtype, dtype_max, fill_value = _get_values(
             values, skipna, fill_value_typ=fill_value_typ, mask=mask
         )
 
-        datetimelike = orig_values.dtype.kind in ["m", "M"]
-
         if (axis is not None and values.shape[axis] == 0) or values.size == 0:
             try:
                 result = getattr(values, meth)(axis, dtype=dtype_max)
@@ -954,12 +966,7 @@ def reduction(
         else:
             result = getattr(values, meth)(axis)
 
-        result = _wrap_results(result, dtype, fill_value)
         result = _maybe_null_out(result, axis, mask, values.shape)
-
-        if datetimelike and not skipna:
-            result = _mask_datetimelike_result(result, axis, mask, orig_values)
-
         return result
 
     return reduction